The use of unmanned vehicles, autonomous robots and the like is appealing for tasks which are dangerous or dull, such as surveillance and patrolling [1], aerial search [9], rescue [2], mapping [19] and more. Manned vehicles may also benefit from partially-automatic operation, such as autopilots in aircraft and automatic parking systems in automobiles. However, increased reliance on such systems increases the reliance on their robustness. Even with validated software, physical faults in sensors and actuators can cause the controlling software to perceive the environment incorrectly, and thus to make decisions that lead to task failure.
This type of fault, where a sensor reading can appear valid, but be invalid given some operational or sensory context, is often referred to as contextual failure [4]. For instance, a sensor may get physically stuck, such that it no longer reports the true nature of its surroundings, but does report a value which is in the range of normally valid readings.
Autonomous robots operate in dynamic environments, where it is usually impossible to foresee, and impractical to account for all possible faults. Instead, the control systems of the robots are complemented by anomaly-detection systems, which can detect anomalies in the robot's systems, and trigger diagnosis (or alert a human operator). To be useful, such a system has to be computationally light (so that it does not create a computational load on the robot, which itself can cause failures), and detect faults with high degree of both precision and recall. A too-high rate of false positives will lead operators to ignoring the system; a too-low rate makes it ineffective. Moreover, the faults must be detected quickly after their occurrence, namely—in real time, so that they can be dealt with before they become catastrophic.
Anomaly detection has generated substantial research over past years. Applications include intrusion and fraud detection, medical applications, robot behavior novelty detection, etc. (see [4] for a comprehensive survey). Anomaly detection in Unmanned (also “Autonomous”) Vehicles (UVs), specifically, is often characterized by a large amount of data from many sensors. The data are typically noisy and streamed online, and requires an anomaly to be discovered quickly, to prevent threats to the safety of the robot [4].
The large amount of data is produced from a large number of system components such as actuators, internal and external sensors, odometry and telemetry, that are each usually monitored at a high frequency. The separately-monitored components can be thought of as dimensions, and thus a collection of monitored readings, at a given point in time, can be considered a multidimensional point (e.g., [12, 15]). Therefore, methods that produce an anomaly score for each given point, can use calculations that consider the points' density, such as Mahalanobis Distance [12] or K-Nearest Neighbor (KNN) [15].
Statistical approaches to anomaly detection are usually considered when large amounts of data are available, and distributions can be calculated. These approaches usually assume that the data is generated from a particular distribution, which is not the case for high dimensional real data sets [4]. Laurikkala et al. [11] proposed the use of Mahalanobis Distance to reduce the multivariate observations to univariate scalars. Brotherton and Mackey [3] use the Mahalanobis Distance as the key factor for determining whether signals measured from an aircraft are of nominal or anomalous behavior. However, it appears that they are limited in the number of dimensions across which they can use the distance, due to run-time issues.
Apart from having to reduce dimensions when using Mahalanobis Distance, the dimensions that are left should be correlated. Recently, Lin et al. [12] demonstrated how using an offline mechanism as the Multi-Stream Dependency Detection (MSDD) [14] can assist in finding correlated attributes in the given data and enable use of Mahalanobis Distance as an anomaly detection procedure. The MSDD algorithm finds correlation between attributes based on their values. Based on the results of the MSDD process, Lin et al. manually defined the correlated attributes for their experiments. However, the main drawback of using the MSDD method is that it consumes many resources and is therefore used with offline training.
To distinguish the inherent noisy data from anomalies, Kalman filters are commonly applied (e.g., [8, 18, 5]). Since simple Kalman filters usually produce a large number of false positives, additional computation is used to determine an anomaly. For example, Cork and Walker [5] present a non-linear model, which, together with Kalman filters, tries to compensate for malfunctioning sensors of UAVs.
There is still a need in the art for online, light and reliable anomaly detection methods, and for devices, robots and the like which incorporate the same.